OSOcean ScienceOSOcean Sci.1812-0792Copernicus PublicationsGöttingen, Germany10.5194/os-14-515-2018Electromagnetic characteristics of ENSOElectromagnetic characteristics of ENSOPetereit Johannespetereit@gfz-potsdam.deSaynischJanhttps://orcid.org/0000-0001-9619-0336IrrgangChristopherhttps://orcid.org/0000-0001-8274-1678WeberTobiasThomasMaik1GFZ German Research Centre
for Geosciences, Potsdam, Germany2Freie Universität Berlin, Institute of Meteorology, Berlin, GermanyJohannes Petereit (petereit@gfz-potsdam.de)25June201814351552410January201828February201823May201830May2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://www.ocean-sci.net/14/515/2018/os-14-515-2018.htmlThe full text article is available as a PDF file from https://www.ocean-sci.net/14/515/2018/os-14-515-2018.pdf

The motion of electrically conducting sea water through Earth's magnetic
field induces secondary electromagnetic fields. Due to its periodicity, the
oceanic tidally induced magnetic field is easily distinguishable in magnetic
field measurements and therefore detectable. These tidally induced signatures
in the electromagnetic fields are also sensitive to changes in oceanic
temperature and salinity distributions. We investigate the impact of oceanic
heat and salinity changes related to the El Niño–Southern Oscillation (ENSO)
on oceanic tidally induced magnetic fields. Synthetic hydrographic
data containing characteristic ENSO dynamics have been derived from a coupled
ocean–atmosphere simulation covering a period of 50 years. The corresponding
tidally induced magnetic signals have been calculated with the 3-D induction
solver x3dg. By means of the Oceanic Niño Index (ONI), based on sea surface
temperature anomalies, and a corresponding Magnetic Niño Index (MaNI),
based on anomalies in the oceanic tidally induced magnetic field at sea
level, we demonstrate that evidence of developing ENSO events can be found in
the oceanic magnetic fields statistically 4 months earlier than in sea
surface temperatures. The analysis of the spatio-temporal progression of the
oceanic magnetic field anomalies offers a deeper understanding on the
underlying oceanic processes and is used to test and validate the initial
findings.

Introduction

The El Niño–Southern Oscillation (ENSO) is well known for its warm and cold
temperature anomalies caused by changes in the ocean–atmosphere system in the
equatorial Pacific Ocean. These anomalous events, known as El Niño and La
Niña, cause extreme weather conditions throughout the globe, e.g. tropical
cyclones , droughts, bush fires and floods
. The extreme weather affects entire ecosystems
and causes damage to infrastructure and
agricultural production , with substantial socio-economic
costs. The prospective doubling of extreme El Niño
events, as a response to greenhouse warming , would increase the
socio-economic costs even further. The negative impacts can be mitigated
with preemptive measures provided reliable El Niño forecasts are available.

Knowledge about spatio-temporal variations of upper-ocean heat content, acquired
traditionally by moorings, are a major source of ENSO predictability
. Monitoring of seawater temperature and salinity
anomalies are consequently a prerequisite for improved ENSO forecasting,
especially since changes in thermocline depth, caused by equatorial Kelvin
waves, have been known to precede sea surface temperature anomalies
. These anomalies are already measurable with an
array of moored buoys (TAO/TRITON) which monitors the upper ocean temperature.
Furthermore, measurement of the mentioned thermocline displacements with
altimetric methods has been subject of extensive research
.

Changes in the oceanic heat content can also be inferred from the motion-induced
electromagnetic fields of the ocean . This new
and lesser-known method complements the pre-existing techniques. It can be
applied to detect mainly large-scale oceanic processes altering temperature and
salinity distributions.

The flow of electrically conducting seawater generates an electric current
due to the interaction of moving salt ions with the geomagnetic field. These
electric currents induce a magnetic field with a local magnitude of several
nanotesla (nT; ).

Depending on the field strength of the ambient geomagnetic field, the magnitudes of ocean flow and electric seawater conductivity determine the ocean tide induced magnetic field strength. The ocean flow is classically divided into general
ocean circulation and ocean tides.

The ocean circulation driven by momentum and buoyancy fluxes is irregular in
time. Its effect are consequently difficult to separate from magnetic field
measurements. However, the circulation-induced magnetic field's non-trivial
contributions to the geomagnetic field have been subject of many studies
.
analysed the influence of changes in the equatorial
current system caused by ENSO on the circulation-induced magnetic field.
They neglected temporal variations in oceanic temperature and salinity and
assumed a time-constant oceanic conductivity distribution. In their study,
the difference between the global circulation-induced magnetic field during
normal conditions and El Niño conditions was estimated. They found that ENSO-related magnetic field anomalies in the equatorial region were too small to be
distinguishable from the magnetic field anomalies caused by variations of the
Antarctic circumpolar current (ACC). The large ACC anomalies extend into the
equatorial region and over-shadow the small effect of ENSO. The magnitude of
the ACC anomalies was found to be of ±0.2 nT at Swarm altitude (430 km) in
the equatorial Pacific and can therefore be assumed to be an upper limit to
ENSO's circulation-induced magnetic field anomalies.

The periodicity of the tidal flow, however, allows
for an easy separation of its magnetic field from other
constituents in geomagnetic field measurements. Its signals have been extracted successfully for the semidiurnal
M2 and N2 tides from measurements of the magnetic satellite missions CHAMP and
Swarm . Amplitude variations of these periodic
magnetic signals are mainly caused by variations in seawater conductivity
distribution . Seawater conductivity is sensitive to seawater temperature and
salinity. In comparison to the amphidromic system of the tides, these
quantities exhibit high temporal variability. Consequently, the majority of
information gained from anomalies of the oceanic tidally induced magnetic
signals are linked to changes in oceanic temperature and salinity
distributions. Modelled and measured tidally induced magnetic fields are in good
agreement , offering the
possibility for in silico sensitivity studies.

The influences of global climate variations, such as Greenland glacial melting
and global warming, on the electromagnetic oceanic tidally induced signals have
already been investigated . For these cases,
the tidally induced radial magnetic field was found to be an appropriate measure
to monitor climate variations of the global oceanic conductivity on decadal
timescales.

With a period of 4–7 years, ENSO acts on monthly to annual timescales.
And despite its impact on the global climate, the immediate climatological
influences on the ocean are limited to the region of the equatorial Pacific.
ENSO's characteristic changes in the ocean circulation alter the Pacific upper
ocean temperature and salinity distributions (≈ 300 m) within months.

In our study, we follow the approach of and investigate
whether the electromagnetic oceanic tidally induced signals could be used as
an appropriate measure for ENSO-induced changes in seawater conductivity and,
consequently, the different stages in the dynamics of ENSO.

In Sect. 2, we introduce the oceanic electromagnetic induction along with
the models and data used to compute the resulting climate sensitive
electromagnetic oceanic tidally induced signals (EMOTSs). Also, based on the
radial magnetic component of the modelled EMOTSs, we define a Magnetic Niño
Index (MaNI). In Sect. 3, we compare the classic Oceanic Niño Index (ONI) to the
proposed MaNI and discuss differences and similarities. We
conclude and summarize the study in Sect. 4.

Models and dataOcean data

We simulated ENSO with the ECHAM6/MPIOM, a global coupled atmosphere–ocean
general circulation model (AOGCM; ).

The Max Planck Institute Ocean Model (MPIOM, ) is a
general ocean circulation model. The model solves the primitive equations for a
hydrostatic Boussinesq fluid on a curvilinear Arakawa C-grid with poles shifted
to Antarctica and Greenland. The ocean is discretized on a grid with a
horizontal resolution of ∼ 3.0∘× 1.8∘ (GR30) and an
irregular vertical distribution over 40 horizontal levels.

The atmosphere general circulation model ECHAM6
is applied with the horizontal resolution of
∼ 3.75∘× 3.75∘ (T31) and 31 vertical hybrid
sigma–pressure levels.

The simulated ocean data covers 50 years of monthly mean seawater temperature
T, seawater salinity S and seawater pressure P. Using the Gibbs seawater
equation (TEOS-10, ), the electric seawater conductivity σ
can be calculated from, T, S and P. Present-day conditions were used to
run the coupled AOGCM in a free mode instead of a observation-driven forcing.
Therefore, the modelled climate represents reality only in a statistical sense.

Oceanic tidally induced currents and EMOTSs

The tidally induced electric current, the source for the electromagnetic oceanic
tidal signals (EMOTSs), is derived for each month with the following two-step
algorithm.

First, the product of seawater conductivity σ and tidal velocities
vM2 is integrated from ocean bottom (-H) to surface
(SSH):
VM2φ,ϑ,t=∫-HSSHσφ,ϑ,z,t⋅vM2φ,ϑ,z,tdz,
where φ, ϑ and z are longitude, co-latitude and depth.
The tidally induced electric current jM2 is then calculated as
the cross-product of the depth-integrated and conductivity-weighted transport
VM2 and the ambient geomagnetic field BEarth
as
jM2φ,ϑ=VM2φ,ϑ×BEarthφ,ϑ.
Variations in the amphidromic system are negligible even on decadal timescales
. Consequently, we followed the approach of
and assumed the tidal system to be invariable in time.
Tidal amplitudes and phases of the oceanic M2 tide were taken from the
TPXO8-atlas .

For this study, the geomagnetic field BEarth was estimated
with the International Geomagnetic Reference Field edition IGRF-12
. Our study focuses on the effects of oceanic conductivity
variations. BEarth is consequently assumed to be constant in
time. Naturally occurring secular variations in BEarth will
linearly vary with jM2 (Eq. ). Since the
variations in the geomagnetic field are well known for near real-time
observations , its effects can be removed before analysing
observational data of EMOTSs for the influence of ENSO.

The electric current jM2 oscillates with the rise and fall of
the tidal velocities. The time-variable magnetic field associated with
jM2 interacts with the electrical conducting environment.
Due to secondary effects, additional electrical currents and electromagnetic
fields are induced. The solution to the posed induction problem, the entirety
of the resulting electromagnetic fields, are EMOTSs . In our study, they are computed at
sea level with the 3-D EM induction solver x3dg . The
solver is based on a contracting volume integral equation approach
.

To realistically model EMOTSs, Earth's electrical mantle conductivity and the
electrical oceanic conductivity need to be included in the model setup
. The mantle conductivity is represented by a time-constant
1-D spherical symmetric conductivity distribution following .
The time-variant ocean conductivity and the constant sediment conductance,
i.e. depth-integrated conductivity, are represented by an inhomogeneous
spherical conductance layer situated on top of the mantle conductivity. This
conductance layer combines sediment conductance and modelled monthly mean ocean
conductance, derived from modelled T, S and P. The sediment conductance is
a combined result of the method of with the global sediment
thickness of .

The strict periodicity of the tidal induction process, and therefore the
resulting EMOTSs, allows for easy extraction of these signals from real
observations. Geomagnetic observations naturally contain contributions of
several magnetic signals and are consequently noisy. Given sufficiently long
time series of measurements, fitting of a specific frequency allows us to
separate even weak signals. Consequently, other non-periodic oceanic processes
such as equatorial Kelvin waves or tropical instability waves are not directly
detectable in noisy observations although their flow is also creating a
magnetic field. However, the changes in temperature and salinity distributions
caused by variations in thermocline depths or travelling patterns of cold and
warm water fronts are detectable through variations in the EMOTSs.

In general, EMOTSs allow one to infer information of oceanic temperature and
salinity distributions throughout the whole water column due to the integrative
nature of the induction process. On annual and decadal timescales, the
variability in the geomagnetic field and the amphidromic system are small
compared to the variability of oceanic temperature and salinity distributions.
Consequently, the variability in jM2, and therefore the
resulting EMOTSs, are mainly caused by changes in the electrical conductivity.
The processes in question, however, need to take place on timescales longer
than the tidal waves in order to be detectable.

Comparable findings are to be expected for all magnetic and electric field
components that form the entirety of EMOTSs. However, in this study we focus
solely on Br, the radial component of the magnetic field of the EMOTSs, at
sea level. Out of the magnetic components it is the only one measurable outside
of the ocean . Br has been observed successfully with
magnetic satellite missions such as CHAMP and Swarm
. Compared to the signal strength at satellite altitude, the
signal is approximately 30 % stronger at sea level. Additionally, substantial
research has already investigated the relationship between oceanic-induced magnetic field variations, especially their radial component, and their
oceanic causes . We add to this canon by investigating
the impact of ENSO, the biggest interannual climate signal, on the oceanic
tidally induced radial magnetic field.

Indices and statistical analysis

Different indices have been used to characterize ENSO events
. A current state-of-the-art indicator is the
ONI from the Climate Prediction Center
of the National Oceanic and Atmospheric Administration (NOAA). The ONI is used
to monitor the oceanic part of the ocean–atmosphere phenomenon called ENSO. It is defined
as a 3-month running mean of sea surface temperature anomalies in the Niño
3.4 region (i.e. 5∘ N–5∘ S, 120–170∘ W)
relative to the mean annual signal of regularly updated 30-year base periods.
Warm and cold events are identified as periods exceeding a threshold of
±0.5 ∘C longer than 4 months. The sea surface temperatures of the
Niño 3.4 region have been known to correlate well with ENSO
.

In our study, the ONI calculations are based on the data of the model climate
experiment conducted with the ECHAM6/MPIOM. Since no significant trends are
present in our data, we used all 50 years as a base period for the ONI
calculation, instead of the running 30-year base period used by NOAA.

We also calculate a comparable index based on the radial tidally induced magnetic
field Br (see Sect. ), the MaNI.
The same algorithm as in the ONI calculation is used with the difference being that
the sea surface temperature anomalies are substituted with Br anomalies in
the Niño 3.4 region.

The relationship of the indices is analysed by calculating their correlation. A
time delay analysis is carried out by calculating and analysing the
cross-correlation. For two time series, the cross-correlation is the evolution
of correlation between those two when they are shifted against each other in
time. It is used to identify temporally lagging or leading signals.

Results and discussionComparison of derived ENSO indices

From our modelled data, we derived two indices (see Sect. ).
First, following the algorithm of the National Oceanic and Atmosphere
Administration (NOAA), we calculated the classic ONI
from sea surface temperatures (SSTs). Then, we adapted the algorithm for the
modelled tidally induced magnetic fields and created a MaNI. The time series of both indices are shown in Fig. .
In agreement with the NOAA classification , 7 El Niños and 10
La Niñas can be found in the climate model data. Following
, one out of the seven El Niños is classified as very
strong (≥ 2.0 ∘C), three are found to be moderate (1.0 to
1.4 ∘C) and three are classified as weak (0.5 to 0.9 ∘C).
The set of La Niña events consists of six moderate (-1.0 to
-1.4 ∘C) and four weak events (-0.5 to -0.9 ∘C).

The strongest El Niño, the very strong warm event, is found at the most
prominent peak of the time series. Starting at month 133 of the modelled time
period, it lasts 16 months and reaches a maximum value of 2.3 ∘C
(Fig. ). These values are comparable to that of the El
Niño events which took place in winter 1997–1998 and 2015–2016, with anomalies of
2.3 ∘C and durations of 13 and 19 months respectively
.

ENSO indices. ONI derived from sea surface temperatures (blue
curve) and MaNI derived from the radial tidally induced magnetic field
Br (red curve). The dashed lines mark the threshold of
±0.5 ∘C, the threshold for El Niño and La Niña events.
The grey shaded area marks the strongest cycle of ENSO events (used for
further analysis). The embedded plot shows the cross-correlation
between ONI and MaNI. For positive leads, MaNI leads ONI.

The spatially averaged temporal mean radial oceanic magnetic field amplitude
(Br) in the Niño 3.4 region was found to be 0.546 nT. The mean seasonal
variation obtained by the climatology is ±0.29 pT (picotesla) and is 3
orders of magnitude smaller than the mean signal. MaNI, based on Br anomalies relative to the 50-year climatology at sea
surface height, has a range of -0.84to 0.82 pT which is in the same
order of magnitude as the seasonal variation.

While the ONI covers the development of sea surface processes, the MaNI also
includes subsurface processes. Bris an integral measure incorporating the
seawater conductivity integrated from ocean bottom to sea surface (see Eqs. and in Sect. ). Despite
their different perspectives on oceanic processes, both indices show a
correlation of 0.63. The SST-based index ONI is used to quantify the duration
and strength of anomalous ENSO events. The high correlation of both indices
shows that ENSO's effects have considerable impact on sea surface processes (ONI) as well as subsurface processes integrated in the tidal magnetic field.

The analysis of the cross-correlation of the two indices (embedded plot in
Fig. ) shows a MaNI lead of 4 months over the ONI.
Accounting for this lead, the correlation of both time series increases to
0.72.

Since in our setup (see Sect. ) the only time-variable
contribution to Br is the seawater conductivity σ, we conclude that
in the Niño 3.4 region subsurface anomalies of σ, caused by anomalies
in S and T, are leading SST anomalies.

Current magnetometers like the absolute scalar magnetometers of the Swarm
mission with accuracy of < 45 pT and a sensitivity of
1 pT /Hz are able to resolve the
global structure of the oceanic tidally induced radial magnetic field. To be
able to observe the presented variations an increase magnetometer precision of
several orders of magnitude down to the femtotesla scale is necessary.

Given the task at hand, two of the most promising magnetometer technologies
are superconducting quantum interference devices (SQUIDs) and spin-exchange
relaxation free (SERF) atomic magnetometer. SQUIDs, which are also used in
technologies such as MRI or magnetoencephalography to detect biomagnetic fields, have achieved
noise levels as low as 0.3 fT /Hz. Also, have presented
an SERF-based atomic magnetometer with a measurement volume 1800 cm3 and
a sensitivity of 0.54 fT /Hz. Additionally, they have
shown that the theoretical achievable fundamental sensitivity is below
0.01 fT /Hz. These exciting improvements however are
still in the laboratory phase where disturbing influences can be controlled way
better than under field conditions. SQUID-based geomagnetic field sensors have
been reported to have reached sensitivities of 6 fT /Hz. Challenges in the calibration of the sensor however
limited the absolute accuracy to 0.3 nT.

Since necessary magnetometer observations are not limited to satellite
measurements, but could also originate from measuring stations deployed at ocean
bottom or an array of moored buoys, there are several options to overcome the
present obstacles in measurability. Given the advancements in magnetic field
sensor technology, it is reasonable to assume that ENSO-induced Br anomalies
investigated in this study might become detectable in the intermediate to far
future. Even more so considering that the absolute accuracy is less important
than the precision in our case, since we investigate periodical variations of
the geomagnetic field. Also, not only are the periodic oceanic tidally induced
radial magnetic field signals easily extracted from geomagnetic field
observations of multiple contributing sources; additionally the error decreases
by n-1/2, where n equals the number of observations in a time series.

Spatio-temporal anomaly development

Temporal and spatial development of SST and Br anomalies of the strongest
ENSO cycle of the time series (grey shaded interval in Fig. ) are displayed as Hovmöller plots
in Fig. .

Considering the large-scale changes in ocean temperature and salinity due to
ENSO, large ocean conductance and consequently considerable Br anomalies are
to be expected. In fact, the range in MaNI is approximately 3 times the
range of the seasonal cycle in the same region (cf. Sect. ).
However, the changes of these surface processes are small compared to the total
conductance, integrated from ocean bottom to sea surface. Hence, the anomalies
in Br considered here amount to less than 1 % of the total signal.

The presence of the geomagnetic equator in the equatorial Pacific region
creates an additional hindrance. The vertical component of
BEarth, the geomagnetic field component inducing Br,
undergoes a change of sign here and vanishes. Consequently, Br vanishes
at the geomagnetic equator and expresses small values in its immediate
proximity.

Also, the M2 tidal flow is not homogeneously distributed throughout the
equatorial Pacific. Local maxima and minima in the tidal flow cause an
additional modulation of the radial tidally induced magnetic field.

To diminish these disturbing influences, Br anomalies have been spatially
averaged from 5∘ S to 5∘ N. SST anomalies have also been
averaged over that range for comparability of both images. Vertical lines
in Fig. b at ≈ 260∘ W are remaining
artefacts caused by the southward dip in the geomagnetic equator in that
region.

The comparison of Fig. a, b shows that positive Br anomalies emerge almost a
year before they form in SST (phase I). The same is found for negative Br
anomalies. They emerge months before positive SST anomalies recede and mark the
end of El Niño (phase III). Although the presented image shows only the
spatio-temporal progression of the strongest cycle of an El Niño followed by
a La Niña, these findings are comparable for the other regular ENSO cycles
(not shown).

With the beginning of phase I, a positive Br anomaly is found west of the
Niño 3.4 region, while at sea surface cold or neutral conditions are found.
Then positive Br anomalies travel through the Niño 3.4 region eastwards.
They are probably caused by equatorial Kelvin waves which are known to precede
the onset of El Niño . Kelvin waves deepen the
thermocline and increase the amount of warm seawater in the water column. A
rise in seawater conductance, the depth-integrated seawater conductivity, is
the consequence. SST anomalies have not yet formed during this phase. An
intensification of positive Br anomalies on the South American west coast is
observed with the arrival of the Kelvin waves several months before the
ONI-defined start of El Niño. This can be explained by the deepening of the
thermocline and the corresponding anomalous increase of warm
water in the upper ocean.

During phase II, El Niño effects become apparent at the sea surface. Here,
the typical El Niño weakening of trade winds leads to changes of wind
patterns in the Walker circulation and alters the equatorial ocean current
system . As a consequence, the warm water of the western Pacific warm pool flows eastward and leads to an increase and westward expansion of
SST anomalies at the Peruvian coast. The eastward migrating warm water causes
a thermocline shallowing in the western warm pool and a simultaneous deepening
of the thermocline at the Peruvian coast. This leads to negative (positive)
Br anomalies west (east) of the Niño 3.4 region reaching local amplitudes
of -6 (3 pT) when El Niño is fully developed. The general agreement
of ONI and MaNI during this phase and the co-occurring maxima of both indices
show that sea surface and subsurface dynamics exhibit similar behaviour under
the influence of a common cause.

The beginning of phase III is marked by an eastward expansion of the western
negative Br anomaly that formed during phase II. The effects of El
Niño, in form of warm SST anomalies, are still present for several months.
Subsurface processes, however, cause an early decrease in Br anomalies and
consequently in MaNI. The eastern positive Br anomaly recedes and a negative
anomaly forms months before the onset of La Niña becomes apparent in ONI.

Phase IV marks the beginning of La Niña at sea surface. The Walker
circulation returns to normal conditions and the westward direction of the
equatorial ocean current is re-established. Hence, the eastern thermocline
shallows due to upwelling of cold water and warm surface water is transported
to the western warm pool. Westward-travelling SST and Br anomalies result in the increased agreement of ONI and MaNI.

With the end of phase IV a new cycle starts. The build-up of positive Br
anomalies, as described in phase I, can be observed towards the end of the
plotted time interval.

The analysis shows that the identified lead in the MaNI is not just a mere
forward shift of the signals but a combined result of multiple effects. We
found that it is a combination of early signs for the onset of the El Niño
probably caused by eastward-travelling Kelvin waves and a decrease in the
magnetic signal months before the actual end of El Niño due to a shallowing
of the thermocline. Consequently, the cycle of Br anomalies and SST
anomalies are phase shifted.

Cross-correlation ONI and conductance

The findings obtained from calculating the cross-correlation between the
oceanic conductance and the ONI at each grid point are summarized in Fig. .

In Fig. a, the maximum conductance anomaly of the time
series at each grid point is shown. The magnitude of conductance anomalies is
linked to the magnitude of relative Br changes. The largest signals are
therefore evident in the western warm pool and at the west coast of South
America. In these regions, the thermocline undergoes the largest relative
changes as a result of ENSO. We also find that the conductance anomalies are
elevated in a small band throughout the whole equatorial region. This region is
the passage way of the equatorial Kelvin waves which vary the thermocline
depth.

In Fig. b, the maximum absolute correlation is plotted.
Largest values are found east of the Niño 3.4 region. The correlation,
the conductance and the ONI decrease westwards in a tongue-shaped pattern,
like the typical SST anomalies of El Niño and La Niña.

Cross-correlation analysis between the ONI and the conductance
(σint) at each grid point: maximum absolute
conductance anomaly (a), absolute maximum correlation, the
peak value of the cross-correlation (b), corresponding
lead/lag to the absolute maximum correlation (c). The
solid rectangle shows the location of the Niño 3.4 region,
the dashed rectangle shows the location of an updated MaNI
(5∘ N–5∘ S, 150–170∘ W).

Figure c shows the lag between the ONI and the conductance. The
lead in conductance increases in a tongue-shaped pattern originating from the
South American west coast. Since the Kelvin waves travel eastwards, an increase
in the lead towards their origin is a logic consequence.

Comparison of time series of ONI (blue) and updated MaNI (red). Anomaly
strength and correlation are reduced, while the lead increases.

For the Niño 3.4 region (solid rectangles in Fig. ), we find the same characteristics as for the
analysis in Sect. . The maximum absolute
correlation of the whole region ranges from ≈ 0.7 to ≈ 0.8
(Fig. b). The lead distribution in the area is not uniform. A
large part of the western half leads by 5 months and decreases eastward to
2 months. The area-averaged Br anomalies of the MaNI therefore produce a
signal that leads statistically by 4 months with a correlation between 0.7
and 0.8.

Qualitative application of findings

The robustness of our findings is tested with a reanalysis of the correlation
between ONI and MaNI using an updated averaging region for the MaNI. The new
region is located at 5∘ N–5∘ S, 150∘–170∘ W. It keeps the poleward extent of ±5∘, which accounts for the presence of the geomagnetic equator in order to assure an adequate averaging. The eastern boundary is shifted westwards to increase the lead in
magnetic field anomalies over SST anomalies (see Fig. c). The
westward shift is constrained by the maximum correlation found in Fig. b. A recalculation of the MaNI within the updated region is
shown in Fig. .

The reanalysis shows an overall decrease in correlation between the two time
series. For a lag of 0 months, the correlation is decreased from 0.63
to 0.38. The maximum correlation is decreased from 0.72 to 0.58, while the lead
is increased from 4 to 5 months. Additionally, the range of the updated MaNI
shrank from -0.84 to 0.82 pT to a range of -0.69 to 0.61 pT.
These results are in agreement with the previous findings of Sect. . We conclude that the lead in MaNI found
in Sect. and is caused by
the lead in conductance anomalies found in Sect. .

Correlation and cross-correlation are methods
for determining the linearity of the relationship between two variables. ENSO is the defining influence on the progression of
ONI and MaNI. However, we found that the processes contributing to ENSO cause
differing developments in sea surface and subsurface dynamics. Consequently,
the decrease in correlation should be viewed as an increase in information
gained from the perspectives of SST and Br anomalies on the same
phenomenon.

Conclusions

Seawater temperature and salinity altering processes are known to be integrated
in the electromagnetic oceanic tidal signals from bottom to surface. We
investigated whether the tidally induced magnetic fields could be used as an
indicator for the El Niño–Southern Oscillation.

We used a coupled ocean–atmosphere general circulation model to simulate 50
years of monthly mean seawater temperature, salinity and pressure
distributions. The properties were used to calculate the tidal electromagnetic
signals for each month.

We analysed the relationship between electromagnetic signals and ENSO by comparing two
ENSO indices. These indices, calculated in the Niño 3.4 region, are the
ONI, based on SST anomalies, and the proposed Magnetic
Niño Index (MaNI), based on anomalies in the tidal magnetic field. We show
that both indices are highly correlated and MaNI leads the ONI by 4 months.

In order to explain this lead, the spatial and temporal evolution of Br
anomalies was analysed and compared with the evolution of SST anomalies. We
found the lead to be explainable with eastward-travelling equatorial Kelvin
waves. They are known to precede the development of typical ENSO SST anomalies.
They also increase the thermocline depth in the eastern Pacific ocean. In
consequence, the electric conductance of the upper ocean is elevated which
results in a stronger tidal magnetic field.

Based on these results, we analysed the relationship between the ONI and equatorial
Pacific conductance anomalies. The spatial distributions of correlation, lead
and signal strength were in good agreement with the found MaNI characteristics.
We showed that correlation of conductance anomalies and ONI increases eastward,
while the lead over the ONI increases westward.

With these findings we updated the averaging region for the recalculation of
the MaNI. With the new index, our interpretation was confirmed and the lead in
MaNI was increased to 5 months. At the same time, signal strength and
correlation were reduced. The decrease in correlation is interpreted as a gain
in information about subsurface dynamics of ENSO rather than a loss in
information about ENSO itself. Traditionally, researchers have focused on sea
surface dynamics for signs of El Niño. The latest research, however, shows
that subsurface dynamics play a crucial role in the build-up phase and the
decline of El Niño. An increased focus on subsurface processes is therefore
necessary to understand ENSO completely.

With the modelled tidal magnetic field anomalies being too small to be
detectable with contemporary measuring methods, the presented results are not
applicable at present times. However, magnetometer sensitivity under laboratory
conditions have reached noise levels several orders of magnitude below the
necessary detection threshold for the presented Br anomalies. Consequently,
a detection of the analysed signals is at least theoretical possible, even
though it might be improbable due to technical limitations in field
measurements.

In summary, our study shows that the dynamic of tidally induced radial magnetic
field anomalies contains information for an early awareness of developing
anomalous warm and cold ENSO conditions. Given that the substantial improvements in observation techniques lead to observations of these signals, this could be used to improve current ENSO warning systems and mitigate its negative impacts.

The research data are available at
ftp://ftp.gfz-potsdam.de/home/ig/petereit/Data_4_Publications/ENSO.nc.

JS developed the concept for the study. TW performed the climate experiment
and provided necessary ENSO data. JP, JS and CI designed all numerical
experiments, which where performed by JP. The manuscript was written by JP
with the assistance of JS, CI, TW and MT. All authors discussed the
results and commented on the manuscript.

The authors declare that they have no conflict of
interest.

Acknowledgements

This study is funded by the German Research Foundation's priority programme
1799 “Dynamic Earth” and by the Helmholtz Association of German Research
Centres. We also want to thank the Max Planck Institute for Meteorology for
making their climate model ECHAM6/MPIOM available to the research community.
The model experiments were carried out at the supercomputing system of the
German Climate Computation Centre (DKRZ) in Hamburg, Germany. We acknowledge
the generation and distribution of the TPXO tidal data. Last but not least, we
want to thank Alexey Kuvshinov for kindly providing his 3-D EM induction code
and the model of the mantle conductivity. The work would not have been able
without his training and help. Thank you.
The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association.
Edited by: Neil Wells
Reviewed by: two anonymous referees